MDAN: Mirror Difference Aware Network for Brain Stroke Lesion Segmentation

Bao, Qiqi; Mi, Shiyu; Gang, Bowen; Yang, Wenming; Chen, Jie; Liao, Qingmin

doi:10.1109/jbhi.2021.3113460

Cited by 13 publications

(2 citation statements)

References 53 publications

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“…Modern applications of artificial intelligence are designed to help humans solve a variety of problems. CNN is one of the developing subcategories of deep learning, which is now widely used in neuroimaging [12]. Deep learning techniques and the use of CNN are being evaluated as a strategy for diagnosing acute ischemic strokes.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages

Tursynova¹,

Omarov²,

Tukenova³

et al. 2023

Computers, Materials &Amp; Continua

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In the field of stroke imaging, deep learning (DL) has enormous untapped potential. When clinically significant symptoms of a cerebral stroke are detected, it is crucial to make an urgent diagnosis using available imaging techniques such as computed tomography (CT) scans. The purpose of this work is to classify brain CT images as normal, surviving ischemia or cerebral hemorrhage based on the convolutional neural network (CNN) model. In this study, we propose a computer-aided diagnostic system (CAD) for categorizing cerebral strokes using computed tomography images. Horizontal flip data magnification techniques were used to obtain more accurate categorization. Image Data Generator to magnify the image in real time and apply any random transformations to each training image. An early stopping method to avoid overtraining. As a result, the proposed methods improved several estimation parameters such as accuracy and recall, compared to other machine learning methods. A python web application was created to demonstrate the results of CNN model classification using cloud development techniques. In our case, the model correctly identified the drawing class as normal with 79% accuracy. Based on the collected results, it was determined that the presented automated diagnostic system could be used to assist medical professionals in detecting and classifying brain strokes.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages

Tursynova¹,

Omarov²,

Tukenova³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Modern applications of artificial intelligence are designed to help humans solve various problems. CNN is a subcategory of deep learning development that is now widely used in neuroimaging [5]. Deep learning techniques and the use of CNN are often used to diagnose acute ischemic stroke.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Classifying Intracerebral Hemorrhage (ICH) Based on Diffusion Weighted –Magnetic Resonance Imaging (DW-MRI)

Nugroho,

Priyanto,

Nugraheni

2023

IJACSA

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Stroke is a condition where the blood supply to the brain is cut off. This occurs due to the rupture of blood vessels in the intracerebral area or Intracerebral Hemorrhage (ICH). Examination by health workers is generally carried out to get an overview of the part of the brain of a patient who has had a stroke. The weakness in diagnosing this disease is that deeper knowledge is needed to classify the type of stroke, especially ICH. This study aims to use the Modified Layers Convolutional Neural Network (ML-CNN) method to classify ICH stroke images based on Diffusion-Weighted (DW) MRI. The data used in this study is a DWI stroke MRI image dataset of 3,484 images. The data consists of 1,742 normal and ICH images validated by a radiologist. Because the data used is relatively small and takes into account the computational time, Stochastic Gradient Descent (SGD) is used. This study compares the basic CNN model scenario with the addition of layers to the original CNN model to produce the highest accuracy value. Furthermore, each model is cross-validated with a different k to produce performance in each model as well as changes to batch size and epoch and comparison with machine learning models such as SVM, Random Forest, Extra Trees, and kNN.The results showed that the smaller the number of batch sizes, the higher the accuracy value and the number of epochs, the higher the number of epochs, the higher the accuracy value of 99.86%. Then, four machine learning methods with accuracy, sensitivity, and specificity below 90% are all compared to CNN2. As a summary of this research, the proposed CNN modification works better than the four machine learning models in classifying stroke images.

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Segmentation of stroke lesions using transformers‐augmented MRI analysis

Ahmed,

Al Shehhi,

Werghi

et al. 2024

Human Brain Mapping

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Accurate segmentation of chronic stroke lesions from mono‐spectral magnetic resonance imaging scans (e.g., T1‐weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the lesions. Due to this inherent spatial heterogeneity, existing machine learning methods have shown moderate performance for chronic lesion delineation. In this study, we introduced: (1) a method that integrates transformers' deformable feature attention mechanism with convolutional deep learning architecture to improve the accuracy and generalizability of stroke lesion segmentation, and (2) an ecological data augmentation technique based on inserting real lesions into intact brain regions. Our combination of these two approaches resulted in a significant increase in segmentation performance, with a Dice index of 0.82 (±0.39), outperforming the existing methods trained and tested on the same Anatomical Tracings of Lesions After Stroke (ATLAS) 2022 dataset. Our method performed relatively well even for cases with small stroke lesions. We validated the robustness of our method through an ablation study and by testing it on new unseen brain scans from the Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset. Overall, our proposed approach of transformers with ecological data augmentation offers a robust way to delineate chronic stroke lesions with clinically relevant accuracy. Our method can be extended to other challenging tasks that require automated detection and segmentation of diverse brain abnormalities from clinical scans.

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MDAN: Mirror Difference Aware Network for Brain Stroke Lesion Segmentation

Cited by 13 publications

References 53 publications

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages

A New Method for Classifying Intracerebral Hemorrhage (ICH) Based on Diffusion Weighted –Magnetic Resonance Imaging (DW-MRI)

Segmentation of stroke lesions using transformers‐augmented MRI analysis

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